SoroushVahidi/scidocs huggingface dataset:
LSTM-Based Hierarchical Denoising Network for Android Malware Detection
(2018)

Mobile security is an important issue on Android platform. Most malware detection methods based on machine learning models heavily rely on expert knowledge for manual feature engineering, which are still difficult to fully describe malwares. In this paper, we present LSTM-based hierarchical denoise network (HDN), a novel static Android malware detection method which uses LSTM to directly learn from the raw opcode sequences extracted from decompiled Android files. However, most opcode sequences are too long for LSTM to train due to the gradient vanishing problem. Hence, HDN uses a hierarchical structure, whose first-level LSTM parallelly computes on opcode subsequences (we called them method blocks) to learn the dense representations; then the secondlevel LSTM can learn and detect malware through method block sequences. Considering that malicious behavior only appears in partial sequence segments, HDN uses method block denoise module (MBDM) for data denoising by adaptive gradient scaling strategy based on loss cache. We evaluate and compare HDN with the latest mainstream researches on three datasets. The results show that HDN outperforms these Android malware detection methods,and it is able to capture longer sequence features and has better detection efficiency thanN-gram-based malware detection which is similar to our method.
A Study on False Channel Condition Reporting Attacks in Wireless Networks
(2014)

Wireless networking protocols are increasingly being designed to exploit a user's measured channel condition; we call such protocols channel-aware. Each user reports the measured channel condition to a manager of wireless resources and a channel-aware protocol uses these reports to determine how resources are allocated to users. In a channel-aware protocol, each user's reported channel condition affects the performance of every other user. The deployment of channel-aware protocols increases the risks posed by false channel-condition feedback. In this paper, we study what happens in the presence of an attacker that falsely reports its channel condition. We perform case studies on channel-aware network protocols to understand how an attack can use false feedback and how much the attack can affect network performance. The results of the case studies show that we need a secure channel condition estimation algorithm to fundamentally defend against the channel-condition misreporting attack. We design such an algorithm and evaluate our algorithm through analysis and simulation. Our evaluation quantifies the effect of our algorithm on system performance as well as the security and the performance of our algorithm.
Extreme-angle broadband metamaterial lens.
(2010)

For centuries, the conventional approach to lens design has been to grind the surfaces of a uniform material in such a manner as to sculpt the paths that rays of light follow as they transit through the interfaces. Refractive lenses formed by this procedure of bending the surfaces can be of extremely high quality, but are nevertheless limited by geometrical and wave aberrations that are inherent to the manner in which light refracts at the interface between two materials. Conceptually, a more natural--but usually less convenient--approach to lens design would be to vary the refractive index throughout an entire volume of space. In this manner, far greater control can be achieved over the ray trajectories. Here, we demonstrate how powerful emerging techniques in the field of transformation optics can be used to harness the flexibility of gradient index materials for imaging applications. In particular we design and experimentally demonstrate a lens that is broadband (more than a full decade bandwidth), has a field-of-view approaching 180 degrees and zero f-number. Measurements on a metamaterial implementation of the lens illustrate the practicality of transformation optics to achieve a new class of optical devices.
HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users
(2016)

We introduce hand movement, orientation, and grasp (HMOG), a set of behavioral features to continuously authenticate smartphone users. HMOG features unobtrusively capture subtle micro-movement and orientation dynamics resulting from how a user grasps, holds, and taps on the smartphone. We evaluated authentication and biometric key generation (BKG) performance of HMOG features on data collected from 100 subjects typing on a virtual keyboard. Data were collected under two conditions: 1) sitting and 2) walking. We achieved authentication equal error rates (EERs) as low as 7.16% (walking) and 10.05% (sitting) when we combined HMOG, tap, and keystroke features. We performed experiments to investigate why HMOG features perform well during walking. Our results suggest that this is due to the ability of HMOG features to capture distinctive body movements caused by walking, in addition to the hand-movement dynamics from taps. With BKG, we achieved the EERs of 15.1% using HMOG combined with taps. In comparison, BKG using tap, key hold, and swipe features had EERs between 25.7% and 34.2%. We also analyzed the energy consumption of HMOG feature extraction and computation. Our analysis shows that HMOG features extracted at a 16-Hz sensor sampling rate incurred a minor overhead of 7.9% without sacrificing authentication accuracy. Two points distinguish our work from current literature: 1) we present the results of a comprehensive evaluation of three types of features (HMOG, keystroke, and tap) and their combinations under the same experimental conditions and 2) we analyze the features from three perspectives (authentication, BKG, and energy consumption on smartphones).
Accurate nonrigid 3D human body surface reconstruction using commodity depth sensors
(2018)

1Department of Computer Science, School of Engineering and Applied Science, Institute for Computer Graphics, The George Washington University, 800 22nd Street NW Suite 3400, Washington, DC 20052, USA 2Department of Epidemiology and Biostatistics, Milken Institute School of Public Health, The George Washington University, 800 22nd Street NW Suite 7680, Washington, DC 20052, USA 3Department of Computer Science, School of Engineering and Applied Science, and Department of Pediatrics, School of Medicine and Health Sciences, Institute for Computer Graphics, The George Washington University, 800 22nd Street NW Suite 5830, Washington, DC 20052, USA
Transductive Adversarial Networks (TAN)
(2018)

Transductive Adversarial Networks (TAN) is a novel domain-adaptation machine learning framework that is designed for learning a conditional probability distribution on unlabelled input data in a target domain, while also only having access to: (1) easily obtained labelled data from a related source domain, which may have a different conditional probability distribution than the target domain, and (2) a marginalised prior distribution on the labels for the target domain. TAN leverages a fully adversarial training procedure and a unique generator/encoder architecture which approximates the transductive combination of the available sourceand target-domain data. A benefit of TAN is that it allows the distance between the sourceand target-domain label-vector marginal probability distributions to be greater than 0 (i.e. different tasks across the source and target domains) whereas other domain-adaptation algorithms require this distance to equal 0 (i.e. a single task across the source and target domains). TAN can, however, still handle the latter case and is a more generalised approach to this case. Another benefit of TAN is that due to being a fully adversarial algorithm, it has the potential to accurately approximate highly complex distributions. Theoretical analysis demonstrates the viability of the TAN framework.
Modeling, simulation, and development of a robotic dolphin prototype
(2005)

Abilities of sea animals and efficiency of fish swimming are a few of the impressive solutions of nature. In this paper, design, modeling, simulation and development studies of a robotic dolphin prototype, entirely inspired by the bottlenose dolphin (Tursiops truncatus), are presented. The first section focuses on the design principles and core design features of the prototype. In the second section, modeling and simulation studies which consist of hydrodynamics, kinematics and dynamical analysis of the robotic dolphin are presented. Dynamical simulations of the underwater behavior of the prototype are included in this section. The third section focuses on the general prototype development from mechanical construction to control system structure. Finally in the last section, experimental results obtained through the development of the prototype are discussed.
An Empirical Comparison of Topics in Twitter and Traditional Media
(2011)

Twitter as a new form of social media can potentially contain much useful information, but content analysis on Twitter has not been well studied. In particular, it is not clear whether as an information source Twitter can be simply regarded as a faster news feed that covers mostly the same information as traditional news media. In This paper we empirically compare the content of Twitter with a traditional news medium, New York Times, using unsupervised topic modeling. We use a Twitter-LDA model to discover topics from a representative sample of the entire Twitter. We then use text mining techniques to compare these Twitter topics with topics from New York Times, taking into consideration of topic categories and types. We find that although Twitter and New York Times cover similar categories and types of topics, the distributions of topic categories and types are quite different. Furthermore, there are Twitter-specific topics and NYT-specific topics, and they tend to belong to certain topic categories and types. We also study the relation between the proportions of opinionated tweets and retweets and topic categories and types, and find some interesting dependence. To the best of our knowledge, ours is the first comprehensive empirical comparison between Twitter and traditional news media.
Binary Rewriting of an Operating System Kernel ∗
(2006)

This paper deals with some of the issues that arise in the context of binary rewriting and instrumentation of an operatin g system kernel. OS kernels are very different from ordinary application code in many ways, e.g., they contain a significant amount of hand-written assembly code. Binary rewriting is an attractive approach for processing OS kernel code for several reasons, e.g., it provides a uniform way to handl e heterogeneity in code due to a combination of source code, assembly code and legacy code such as in device drivers. However, because of the many differences between ordinary application code and OS kernel code, binary rewriting techniques that work for application code do not always carry over directly to kernel code. This paper describes some of the issues that arise in this context, and the approaches we have taken to address them. A key goal when developing our system was to deal in a systematic manner with the various peculiarities seen in low-level systems code, and reason about the safety and correctness of code transformation s, without requiring significant deviations from the regular d evelopmental path. For example, a precondition we assumed was that no compiler or linker modifications should be required to use it and the tool should be able to process kernel binaries in the same way as it does ordinary applications.
Towards Context-Aware Interaction Recognition for Visual Relationship Detection
(2017)

Recognizing how objects interact with each other is a crucial task in visual recognition. If we define the context of the interaction to be the objects involved, then most current methods can be categorized as either: (i) training a single classifier on the combination of the interaction and its context; or (ii) aiming to recognize the interaction independently of its explicit context. Both methods suffer limitations: the former scales poorly with the number of combinations and fails to generalize to unseen combinations, while the latter often leads to poor interaction recognition performance due to the difficulty of designing a contextindependent interaction classifier.,,To mitigate those drawbacks, this paper proposes an alternative, context-aware interaction recognition framework. The key to our method is to explicitly construct an interaction classifier which combines the context, and the interaction. The context is encoded via word2vec into a semantic space, and is used to derive a classification result for the interaction. The proposed method still builds one classifier for one interaction (as per type (ii) above), but the classifier built is adaptive to context via weights which are context dependent. The benefit of using the semantic space is that it naturally leads to zero-shot generalizations in which semantically similar contexts (subject-object pairs) can be recognized as suitable contexts for an interaction, even if they were not observed in the training set. Our method also scales with the number of interaction-context pairs since our model parameters do not increase with the number of interactions. Thus our method avoids the limitation of both approaches. We demonstrate experimentally that the proposed framework leads to improved performance for all investigated interaction representations and datasets.
Effective resource management for enhancing performance of 2D and 3D stencils on GPUs
(2016)

GPUs are an attractive target for data parallel stencil computations prevalent in scientific computing and image processing applications. Many tiling schemes, such as overlapped tiling and split tiling, have been proposed in past to improve the performance of stencil computations. While effective for 2D stencils, these techniques do not achieve the desired improvements for 3D stencils due to the hardware constraints of GPU.
 A major challenge in optimizing stencil computations is to effectively utilize all resources available on the GPU. In this paper we develop a tiling strategy that makes better use of resources like shared memory and register file available on the hardware. We present a systematic methodology to reason about which strategy should be employed for a given stencil and also discuss implementation choices that have a significant effect on the achieved performance. Applying these techniques to various 2D and 3D stencils gives a performance improvement of 200-400% over existing tools that target such computations.
Zero-Current Switching Switched-Capacitor Zero-Voltage-Gap Automatic Equalization System for Series Battery String
(2012)

The series battery string or supercapacitor string automatic equalization system based on quasi-resonant switched-capacitor converter is presented in this paper. It realizes the zero-voltage gap between cells and allows maximum energy recovery in a series battery system or supercapacitor system. It not only inherits the advantage of conventional switched-capacitor battery cell balancing system, but also overcomes the drawback of conduction loss, switching loss, and finite voltage difference among battery cells. All switches are MOSFET and controlled by just a pair of complementary signals in synchronous trigger pattern and the resonant tanks operate alternatively between the two states of charging and discharging. Zero-current switching and zero-voltage gap are achieved in this paper. Different resonant tank designs can meet the needs of different balancing time to meet the needs of different energy storage devices. Experimental results indicate that the efficiency of the system is high exceeding 98%. The system is very suitable for balancing used in battery management system.
End-to-end Speech Recognition Using Lattice-free MMI
(2018)

We present our work on end-to-end training of acoustic models using the lattice-free maximum mutual information (LF-MMI) objective function in the context of hidden Markov models. By end-to-end training, we mean flat-start training of a single DNN in one stage without using any previously trained models, forced alignments, or building state-tying decision trees. We use full biphones to enable context-dependent modeling without trees, and show that our end-to-end LF-MMI approach can achieve comparable results to regular LF-MMI on well-known large vocabulary tasks. We also compare with other end-to-end methods such as CTC in character-based and lexicon-free settings and show 5 to 25 percent relative reduction in word error rates on different large vocabulary tasks while using significantly smaller models.
Counting of cigarettes in cigarette packets using LabVIEW
(2014)

The proposed work presents a technique for an automated count of cigarette in cigarette packets. Proposed work is based on application of image processing techniques in LabVIEW platform. An objective of the proposed work is to count the number of cigarettes in a packet. National Instrument's Smart camera is used to capture images of cigarette packets moving in packaging line and process the data to fulfill the above objective. The technique was subjected to offline testing on more than 50 number of cigarette packets and the results obtained are found to be satisfactory in all cases.
LQR, double-PID and pole placement stabilization and tracking control of single link inverted pendulum
(2015)

This paper presents the dynamic behaviour of a nonlinear single link inverted pendulum-on-cart system based on Lagrange Equation. The nonlinear model linearization was presented based on Taylor series approximation. LQR, double-PID and simple pole placement control techniques were proposed for upright stabilization and tracking controls of the system. Simulations results for the various control techniques subjected to a unity magnitude pulse input torque with and without disturbance were compared. The performances of the proposed controllers were investigated based on response time specifications and level of disturbance rejection. Thus, the performance of LQR is more reliable and satisfactory. Finally, future work suggestions were made.
Defect Detection in Textures through the Use of Entropy as a Means for Automatically Selecting the Wavelet Decomposition Level
(2016)

This paper presents a robust method for defect detection in textures, entropy-based automatic selection of the wavelet decomposition level (EADL), based on a wavelet reconstruction scheme, for detecting defects in a wide variety of structural and statistical textures. Two main features are presented. One of the new features is an original use of the normalized absolute function value (NABS) calculated from the wavelet coefficients derived at various different decomposition levels in order to identify textures where the defect can be isolated by eliminating the texture pattern in the first decomposition level. The second is the use of Shannon's entropy, calculated over detail subimages, for automatic selection of the band for image reconstruction, which, unlike other techniques, such as those based on the co-occurrence matrix or on energy calculation, provides a lower decomposition level, thus avoiding excessive degradation of the image, allowing a more accurate defect segmentation. A metric analysis of the results of the proposed method with nine different thresholding algorithms determined that selecting the appropriate thresholding method is important to achieve optimum performance in defect detection. As a consequence, several different thresholding algorithms depending on the type of texture are proposed.
The influence of futures markets on real time price stabilization in electricity markets
(2004)

Markets can interact with power systems in ways that can render an otherwise stable market and an otherwise stable power system into an unstable overall system. This unstable system will be characterized not only by fluctuating prices that do not settle to constant values, but, more worrisome, it creates the possibility of inducing slow electromechanical oscillations if left unchecked. This will tend to happen as a result of "price chasing" on the part of suppliers that can react (and over-react) to changing system prices. This paper examines the role that futures markets may have on the clearing prices and on altering the volatility and potential instability of real time prices and generator output.
Ubiquitous enterprise service adaptations based on contextual user behavior
(2007)

Recent advances in mobile technologies and infrastructures have created the demand for ubiquitous access to enterprise services from mobile handheld devices. Further, with the invention of new interaction devices, the context in which the services are being used becomes an integral part of the activity carried out with the system. Traditional human–computer interface (HCI) theories are now inadequate for developing these context-aware applications, as we believe that the notion of context should be extended to different categories: computing contexts, user contexts, and physical contexts for ubiquitous computing. This demands a new paradigm for system requirements elicitation and design in order to make good use of such extended context information captured from mobile user behavior. Instead of redesigning or adapting existing enterprise services in an ad hoc manner, we introduce a methodology for the elicitation of context-aware adaptation requirements and the matching of context-awareness features to the target context by capability matching. For the implementation of such adaptations, we propose the use of three tiers of views: user interface views, data views, and process views. This approach centers on a novel notion of process views to ubiquitous service adaptation, where mobile users may execute a more concise version or modified procedure of the original process according to their behavior under different contexts. The process view also serves as the key mechanism for integrating user interface views and data views. Based on this model, we analyze the design and implementation issues of some common ubiquitous access situations and show how to adapt them systematically into a context-aware application by considering the requirements of a ubiquitous enterprise information system.
Conditional image generation using feature-matching GAN
(2017)

Generative Adversarial Net is a frontier method of generative models for images, audios and videos. In this paper, we focus on conditional image generation and introduce conditional Feature-Matching Generative Adversarial Net to generate images from category labels. By visualizing state-of-art discriminative conditional generative models, we find these networks do not gain clear semantic concepts. Thus we design the loss function in the light of metric learning to measure semantic distance. The proposed model is evaluated on several well-known datasets. It is shown to be of higher perceptual quality and better diversity then existing generative models.
Reactance-domain ESPRIT algorithm for a hexagonally shaped seven-element ESPAR antenna
(2005)

A direction-of-arrival (DoA) method that combines the reactance-domain (RD) technique and the ESPRIT algorithm is proposed for use with the 7-element electronically steerable parasitic array radiator (ESPAR) for the estimation of noncoherent sources. Simulations show that the method could resolve up to three incoming signals with an estimation performance that depends on the signal's angle of arrival. Moreover, the method is compared with the Cramer-Rao lower bound (CRB) and the MUSIC asymptotic error variance, both modified for the RD technique. Numerical comparison between this lower bound and the MUSIC algorithm confirmed that the proposed method can achieve the CRB and provide high-precision DoA estimation with a level of performance that is sufficient for many DoA finding applications. The proposed method could be demonstrated by means of experiments on DOA estimation conducted in an anechoic chamber.